It’s hard to go online these days without reading about generative AI: the content-producing technology behind the likes of ChatGPT and Claude, used by millions for content creation and surfing the web. While AI has been used by businesses for decades, often for highly specific and unseen tasks, it was generative AI that thrust artificial intelligence into the mainstream. It did so by making the power of AI truly tangible for the public.
A similar process is occurring in the retail sector. Not with generative AI, which thousands of retailers are already using to great effect, but with agentic AI. Agentic AI is becoming the new automated colleague used by retailers to improve efficiency and streamline their customer experience processes. While generative AI can act as the creative engine behind a company, agentic AI takes things a step further by independently performing tasks. And as these benefits and abundant use cases are becoming more tangible for retail leaders, this technology is becoming difficult to ignore.
The agentic advantage
Let’s be clear. Generative AI has had a transformative impact on many retailers’ customer experience processes. Whether it’s automatically replying to customer questions, drafting personalised emails, or helping to craft targeted marketing campaigns, it’s a great tool for producing content quickly. But it has some clear limits: without direct access to a company’s systems or customer data, the output can be generic and lack the context required to be truly useful.
Agentic AI goes that step further. Because Agentic AI can be integrated into a business’s existing systems, it can start to perform tasks on its own. It can do things like retrieve customer data, create a return label, or start a payment, all without human input. It does what a human employee would do, only in an automated way. The main difference from generative AI is this integration. Agentic AI can work with non-public data such as order histories, CRM records, or inventory levels. If a customer asks when an order will arrive, the agent checks the order system and gives a specific answer. Generative AI focuses on creating content, while agentic AI makes processes smarter and faster by executing them end-to-end.
This integration is priceless, and it is behind the surge in interest we are seeing in agentic AI technology in lots of sectors, especially retail. With agentic AI in place, basic enquiries or issues do not need to be escalated to a human agent. An AI agent can instead provide personalised answers and handle requests independently. The result is shorter wait times, less pressure on support teams, and a smoother experience for customers.
The push and pull behind agentic AI’s rise
For most of 2025, adopters of Agentic AI have been in the experimental phase. That is, exploring what’s possible and focusing on isolated use cases: one department, one process, or a small team trying an AI agent. It has, for the most part, been a period of discovery and learning. This is reminiscent of the early days of personalisation. At first, marketers made small tweaks, like product recommendations in emails. Over time, personalisation spread across the entire customer journey.
However, as the companies that use AI agents to answer enquiries or automate repetitive tasks save time, gain flexibility, and improve service quality, confidence in this technology is growing. Some hesitation in automated systems is inevitable. This is, after all, the handing over of many crucial aspects of the customer journey to an automated agent. But the proof has been in the pudding: companies are benefiting and more are being pulled towards adoption.
Let’s consider some of these pulls for retailers. First, agentic AI can improve customer experience by guiding shoppers through their journey, using in-house data to make suggestions that fit past preferences and stock in real time. Second, these systems can cut costs for retailers by automating tasks that human agents used to handle. Think returns, order updates, and troubleshooting, which can all be automated in a way that reduces handling time and errors. Third, agentic agents can elevate customer conversion. They reduce choice overload, so customers reach the right product faster, with side-by-side comparisons, back-in-stock options, and timely prompts to complete the purchase. And fourth, agentic AI can extend the services a retailer provides. It allows retailers to move beyond just answering queries to actioning them – imagine if arduous processes like updating customer details, scheduling store visits or fittings, sending secure payment links, and confirming delivery or click-and-collect were handled by an automated agent.
However, as well as the pull factor of increasingly powerful agentic AI systems for retailers, consumer behaviour is also acting as a push towards automated agents. In 2025 customers expect seamless, natural communication with businesses, whether through chat, voice, or other interactive interfaces. For many companies agentic AI is becoming a non-negotiable to meet these expectations in an efficient, cost-effective way.
From small steps to structural adoption
To truly realise these advantages, however, businesses must reach a point of structural adoption: a situation in which AI agents are deployed on a structural level across multiple departments. In effect this just means automating several processes at once. This drives benefits because agentic AI systems produce agents that can collaborate, share context, and support employees consistently across teams.
While having AI agents handle simple, defined tasks in one area is beneficial, having these systems embedded across departments and systems, and eventually organisation-wide, gets the most out of them. For one, it would unlock faster decision-making by processing real-time data and surfacing clear options across different areas. It would also lower operational costs as routine workflows run automatically with fewer errors, and elevate customer experiences with instant, personalised interactions that complete tasks end to end. Finally, it would lift productivity by freeing employees to focus on exceptions, problem-solving, and higher-value work.
Of course, for many retailers there are more modest steps to take before thinking about structural adoption. But having the end goal and its benefits in mind when undertaking this process is key. It must also be said that integrating this technology is often easier than many might think. Setting up an entire AI-driven customer service team to reap the benefits is not necessary. Rather retailers should start small, allow one agent to take over a specific task, and scale from there. Once the benefits become tangible, the case for further integration will speak for itself.
